A novel ant colony algorithm for solving shortest path problems with fuzzy arc weights

The aviation industry is one of the most widely used applications in transportation. Due to the limited capacity of aircraft, revenue management in this industry is of high significance. On the other hand, the hub location problem has been considered to facilitate the demands assignment to hubs. This paper presents an integrated p-hub location and revenue management problem under uncertain demand to maximize net revenue and minimize total cost, including hub establishment and transportation costs. A fuzzy programming model and a genetic algorithm are developed to solve the proposed model with different sizes. The mining and petroleum industry is used for case studies. Results show that the proposed algorithm can obtain a suitable solution in a reasonable amount of time.

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“…Di Caprio et al [27] developed an ant colony algorithm under uncertainty for the shortest path method problem.…”

Section: Literature Reviewmentioning

confidence: 99%

EFP‐GA: An Extended Fuzzy Programming Model and a Genetic Algorithm for Management of the Integrated Hub Location and Revenue Model under Uncertainty

2022

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“…Another type of VRP problems are the Shortest Path (SP) models. For example, Di Caprio et al (2021) have proposed a routing model in which the weights given to the selected paths are fuzzy and indeterministic, and the proposed model is solved by three algorithms of genetic, particle swarm and ants, and the numerical results are compared. Ebrahimnejad et al (2021) have presented a model of uncertain SP that estimates the Mixed Interval-Valued Fuzzy interval and solve it using MOPSO algorithm, modified artificial bee colony (MABC) algorithm and NSGAII.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling a robust multi-objective locating-routing problem with bounded delivery time using meta-heuristic algorithms

Hashemi

Mahmoodi

Jasemi

et al. 2021

SRT

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Purpose This study aims to investigate a locating-routing-allocating problems and the supply chain, including factories distributor candidate locations and retailers. The purpose of this paper is to minimize system costs and delivery time to retailers so that routing is done and the location of the distributors is located. Design/methodology/approach The problem gets closer to reality by adding some special conditions and constraints. Retail service start times have hard and soft time windows, and each customer has a demand for simultaneous delivery and pickups. System costs include the cost of transportation, non-compliance with the soft time window, construction of a distributor, purchase or rental of a vehicle and production costs. The conceptual model of the problem is first defined and modeled and then solved in small dimensions by general algebraic modeling system (GAMS) software and non-dominated sorting genetic algorithm II (NSGAII) and multiple objective particle swarm optimization (MOPSO) algorithms. Findings According to the solution of the mathematical model, the average error of the two proposed algorithms in comparison with the exact solution is less than 0.7%. Also, the algorithms’ performance in terms of deviation from the GAMS exact solution, is quite acceptable and for the largest problem (N = 100) is 0.4%. Accordingly, it is concluded that NSGAII is superior to MOSPSO. Research limitations/implications In this study, since the model is bi-objective, the priorities of decision makers in choosing the optimal solution have not been considered and each of the objective functions has been given equal importance according to the weighting methods. Also, the model has not been compared and analyzed in deterministic and robust modes. This is because all variables, except the one that represents the uncertainty of traffic modes, are deterministic and the random nature of the demand in each graph is not considered. Practical implications The results of the proposed model are valuable for any group of decision makers who care optimizing the production pattern at any level. The use of a heterogeneous fleet of delivery vehicles and application of stochastic optimization methods in defining the time windows, show how effective the distribution networks are in reducing operating costs. Originality/value This study fills the gaps in the relationship between location and routing decisions in a practical way, considering the real constraints of a distribution network, based on a multi-objective model in a three-echelon supply chain. The model is able to optimize the uncertainty in the performance of vehicles to select the refueling strategy or different traffic situations and bring it closer to the state of certainty. Moreover, two modified algorithms of NSGA-II and multiple objective particle swarm optimization (MOPSO) are provided to solve the model while the results are compared with the exact general algebraic modeling system (GAMS) method for the small- and medium-sized problems.

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“…Compared to many previous meta-heuristic methods [3], the ACO method [DORIGO, 1992] can identify the shortest way in very little time and at a lower price. The ACO is a class of optimizing metaheuristics influenced by bio-inspired algorithms (particularly ants' movement or similar creatures that create a remarkably).…”

Section: Introductionmentioning

confidence: 99%

Energy Efficient Networks Using Ant Colony Optimization with Game Theory Clustering

Gunigari¹,

Chitra²

2023

Intelligent Automation &Amp; Soft Computing

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Real-time applications based on Wireless Sensor Network (WSN) technologies quickly lead to the growth of an intelligent environment. Sensor nodes play an essential role in distributing information from networking and its transfer to the sinks. The ability of dynamical technologies and related techniques to be aided by data collection and analysis across the Internet of Things (IoT) network is widely recognized. Sensor nodes are low-power devices with low power devices, storage, and quantitative processing capabilities. The existing system uses the Artificial Immune System-Particle Swarm Optimization method to minimize the energy and improve the network's lifespan. In the proposed system, a hybrid Energy Efficient and Reliable Ant Colony Optimization (ACO) based on the Routing protocol (E-RARP) and game theory-based energy-efficient clustering algorithm (GEC) were used. E-RARP is a new Energy Efficient, and Reliable ACO-based Routing Protocol for Wireless Sensor Networks. The suggested protocol provides communications dependability and high-quality channels of communication to improve energy. For wireless sensor networks, a game theory-based energy-efficient clustering technique (GEC) is used, in which each sensor node is treated as a player on the team. The sensor node can choose beneficial methods for itself, determined by the length of idle playback time in the active phase, and then decide whether or not to rest. The proposed E-RARP-GEC improves the network's lifetime and data transmission; it also takes a minimum amount of energy compared with the existing algorithms.

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A novel ant colony algorithm for solving shortest path problems with fuzzy arc weights

Cited by 99 publications

References 32 publications

EFP‐GA: An Extended Fuzzy Programming Model and a Genetic Algorithm for Management of the Integrated Hub Location and Revenue Model under Uncertainty

EFP‐GA: An Extended Fuzzy Programming Model and a Genetic Algorithm for Management of the Integrated Hub Location and Revenue Model under Uncertainty

Modeling a robust multi-objective locating-routing problem with bounded delivery time using meta-heuristic algorithms

Energy Efficient Networks Using Ant Colony Optimization with Game Theory Clustering

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